Durham University Partner Site
UTOPIAE is a European research and training network looking at cutting edge methods bridging optimisation and uncertainty quantification applied to aerospace systems. The network will run from 2017 to 2021, and is funded by the European Commission through the Marie Skłodowska-Curie Actions of H2020.
The network is made up of 15 partners across 6 European countries, including Durham University in the UK, and one international partner in the USA, collecting mathematicians, engineers and computer scientists from academia, industry, public and private sectors.
The UTOPIAE mission is to:
- Train, by research and by example, 15 Early Stage Researchers in the field of uncertainty quantification and optimisation to become leading independent researchers and entrepreneurs that will increase the innovation capacity of the EU.
- To equip the researchers with the skills they will need for successful careers in academia and industry.
- To develop fundamental mathematical methods and algorithms to bridge the gap between Uncertainty Quantification and Optimisation and between Probability Theory and Imprecise Probability Theory for Uncertainty Quantification to efficiently solve high-dimensional, expensive and complex engineering problems.
There are 2 of the 15 projects which are based at the Department of Mathematical Sciences, Durham University:
Project ESR8: Prediction of System Reliability during Design Phases
Daniel Krpelik is the Early Stage Researcher working on ESR8 with supervisors Prof Frank Coolen and Dr Louis Aslett.
Objectives: To develop suitable theory of system reliability quantification, using imprecise probabilities, in order to reflect carefully the uncertainties involved in this process at different stages; To derive an approximation of the lower and upper probabilities of system functionalities; To upscale to the propagation of upper and lower previsions to large systems; To study a representation of uncertainty in multi-phased design of aerospace systems.
Expected Results: A theoretical and computational framework for system reliability quantification in multi-phase processes using Imprecise Probability Theory. A theoretical and computational framework for robust optimisation and decision making in multi-phase processes. A demonstrative example of application to the life cycle assessment of a launcher. New methods for system reliability quantification at different stages of system design, reflecting indeterminacy in the specification of the required functionality and providing the opportunity to focus on robustness with regard to resilience of the system. New computational methods, including the use of approximations, to enable upscaling of recently present-ed theory of imprecise probabilities for system reliability to large real-world multi-phase processes.
Planned Secondments: SU (M27-29) to work on the application of imprecise probabilities and expert elicitation to the end-to-end design of space systems within WP3.3 and WP3.5, ESTECO (M37-39) to work on the application of the pro-posed methodology to multidisciplinary model-based collaborative system engineering within WP3.4 and 3.5.
Project ESR9: Large Scale Simulation for Quantifying Severe Uncertainty with Imprecise Probabilities
Tathagata Basu is the Early Stage Researcher working on ESR9 with supervisors Dr Jochen Einbeck and Dr Matthias Troffaes.
Objectives: To investigate algorithms and methods for uncertainty quantification using statistical models for highly dimensional data with limited structural knowledge, in a way that leads to computationally efficient yet still reliable inference about the actual risks in the system; To investigate how standard statistical simulation approaches, such as for instance Markov chain Monte Carlo, can be extended to modelling scenarios involving imprecise priors or penalties; To derive theoretical results from small scale tests; To upscale the dimensionality of the application, depending on the results of the small scale tests.
Expected Results: Enabling methods of a non-parametric character to be applied to much larger problems than is currently possible. An efficient computational framework to deal with highly dimensional data with limited structural knowledge. New efficient statistical simulation techniques. Improved representation of model uncertainty in simulation models, leading to better risk-informed decisions.
Planned Secondments: NPL (M19-21) to work on the treatment of experimental data and model validation within WP2.3, VKI (M28-30) to work on high-dimensional uncertainty propagation and the treatment of experimental data within WP2.2 and 2.3.
UTOPIAE opening training school 2017
Opening training event for UTOPIAE ESRs in Glasgow, UK.
Lectures given by Durham staff:
- Prof Frank Coolen - Introduction to Imprecise Probability and Imprecise Statistical Methods
- Prof Frank Coolen - Introduction to System Reliability with Imprecise Probability
- Dr Louis Aslett - Statistical Methods for System Reliability
- Dr Jochen Einbeck - Statistical Modelling and Regularisation
The ESRs begin their research at Durham University in November 2017. Outputs arising from the UTOPIAE@Durham project will be posted here.